On the economics of knowledge creation and sharing

On the economics of knowledge creation and sharing Omar Metwally, MD omar.metwally@gmail.com University of California, San Francisco First Draft: September 11th 2017 Abstract This work bridges the technical concepts underlying distributed computing and blockchain technologies with their profound socioeconomic and sociopolitical implica- tions, particularly on academic research and the healthcare industry. Several examples from academia, industry, and healthcare are explored throughout this paper. The limit- ing factor in contemporary life sciences research is often funding: for example, to pur- chase expensive laboratory equipment and materials, to hire skilled researchers and technicians, and to acquire and disseminate data through established academic chan- nels. In the case of the U.S. healthcare system, hospitals generate massive amounts of data, only a small minority of which is utilized to inform current and future medical practice. Similarly, corporations too expend large amounts of money to collect, secure and transmit data from one centralized source to another. In all three scenarios, data moves under the traditional paradigm of centralization, in which data is hosted and cu- rated by individuals and organizations and of benefit to only a small subset of people. Page ! of ! 1 19 1.Introduction In its current siloed state, data is a liability rather than an asset. The value of data de- pends on its quantity and quality. Organizations, including corporations, government, and academia, have few incentives to share data outside the context of selling it. For in- stance, advertisers use data procured from individuals’ browsing history and social media use (via internet service providers, social media and search engines) to create de- tailed profiles of individuals’ online behavior and spending habits and more effective sell products to unknowing consumers. While this paradigm fits naturally into a capital- istic society, these economics of data collection and transfer do not facilitate the genera- tion or sharing of knowledge in the academic setting. A typical university-based research group depends upon external funding to support its research activities. These funds often originate from governmental bodies, philanthropic organizations, or corporations and are difficult to secure [1]. Only a small minority of tenure track scientists ever becomes principal investigators, and a lab that is productive today can become defunct tomorrow if its principal investigator is unable to secure funding for laboratory equipment and supplies such as microscope parts, reagents, and to compensate technicians and trainees [2]. Principal investigators spend a majority of their time writing grant applications rather than participating directly in the process of knowledge generation [3]. It is often said that publications are the currency of academia. The maxim “publish or perish” applies to most research groups, whose work culminates in peer-reviewed pub- lications with publication fees commonly amounting to several thousand dollars [4]. Moreover, these peer-reviewed publications are heavily biased toward so-called “posi- tive results,” in which mathematical correlations between variables are described [5]. Page ! of ! 2 19 The vast majority of data produced by scientific researchers do not refute the null hy- pothesis; in a best case scenario, they are deemed “negative results,” and are discarded; in a worst case scenario, they are data that can’t be replicated, verified, or are outright fraudulent [6]. The result is the modern-day academic machinery. This severely flawed system, a victim of many conflicting economic forces, results in a tremendously ineffi- cient workflow in which most grant money is wasted in the form of negative, and there- fore unpublishable, results. Principal investigators spend a majority of their time trying to secure funding. The ultimate winner is the $10 billion business of academic publish- ing [6]. In this reality, data with the potential to produce vast knowledge is rendered into a vastly wasted opportunity to exponentially build on communities’ resources. In- dividuals’ roles are minimized by the centralization of resources in the hands of a privi- leged few. 2. Background While the term “blockchain” has been touted to near-hysteria in popular media in the context of initial coin offerings and get-rich-quick schemes, an understanding of this data structure’s logic reveals the tremendous and fascinating socioeconomic implica- tions of storing data on blockchain. In its most simplified form, a blockchain is a ledger [7]. The reason for blockchain’s natural association with financial derivatives lies in its ability to mathematically prove the authenticity of data and demonstrate proof of stake and proof of work [8]. The starting port for these use cases is the typical consumer, who is separate from (and often completely unaware of) the data collected a

…(Full text truncated)…

This content is AI-processed based on ArXiv data.